Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components

This paper presents a noise-tolerant electrocardiogram (ECG) beat classification method based on higher order statistics (HOS) of subband components.

Five levels of discrete wavelet transform (DWT) were applied to decompose the signal into six subband components. Higher order statistics proceeded to calculate four sets of HOS features from the three midband components, which together with three RR interval-related features constructed the primary feature set. A feature selection algorithm based on correlation coefficient and Fisher discriminality was then exploited to eliminate redundant features from the primary feature set. A feedforward backpropagation neural network (FFBNN) was employed as the classifier. Two sample selection strategies and four categories of noise artifacts were utilized to justify the capacity of the method.

More than 97.5% discrimination rate was achieved, no matter which of the two sampling selection strategies was used. By using the feature selection method, the feature dimension can be readily reduced from 30 to 18 with negligible decrease in accuracy. Compared with other method in the literature, the proposed method improves the sensitivities of most beat types, resulting in an elevated average accuracy. The proposed method is tolerant to environmental noises; as high as 91% accuracies were retained even when contaminated with serious noises, 10 dB signal-to-noise ration (SNR), of different kinds.

The results demonstrate the effectiveness and noise-tolerant capacities of the proposed method in ECG beat classification.